Advances and Open Problems in Federated Learning, Adria Gascon, Aleksandra Korolova, Ananda Theertha Suresh, Arjun Nitin Bhagoji, Aurelien Bellet, Ayfer Ozgur, Badih Ghazi, Ben Hutchinson, Brendan Ave
Автор: Hong Choong Seon, Khan Latif U., Chen Mingzhe Название: Federated Learning for Wireless Networks ISBN: 9811649626 ISBN-13(EAN): 9789811649622 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy.
Автор: Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu Название: Federated Learning ISBN: 1681736977 ISBN-13(EAN): 9781681736976 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12335.00 р. Наличие на складе: Нет в наличии.
Описание: How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
Автор: Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu Название: Federated Learning ISBN: 1681736993 ISBN-13(EAN): 9781681736990 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 15523.00 р. Наличие на складе: Нет в наличии.
Описание: How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
Описание: Rediscovers, redefines, and reestablishes the most recent applications of federated learning using blockchain and IIoT to optimize data for next-generation networks. The book provides insights to readers in a way of inculcating the theme that shapes the next generation of secure communication.
Автор: Rehman Muhammad Habib Ur, Gaber Mohamed Medhat Название: Federated Learning Systems: Towards Next-Generation AI ISBN: 3030706036 ISBN-13(EAN): 9783030706036 Издательство: Springer Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development.
Описание: Rediscovers, redefines, and reestablishes the most recent applications of federated learning using blockchain and IIoT to optimize data for next-generation networks. The book provides insights to readers in a way of inculcating the theme that shapes the next generation of secure communication.
Описание: This book constitutes the refereed post-conference proceedings of the Second BenchCouncil International Federated Intelligent Computing and Block Chain Conferences, FICC 2020, held in Qingdao, China, in October/ November 2020.The 32 full papers and 6 short papers presented were carefully reviewed and selected from 103 submissions.
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